"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F

__all__ = ['HACNN']


class ConvBlock(nn.Module):
    """Basic convolutional block.
    
    convolution + batch normalization + relu.

    Args:
        in_c (int): number of input channels.
        out_c (int): number of output channels.
        k (int or tuple): kernel size.
        s (int or tuple): stride.
        p (int or tuple): padding.
    """

    def __init__(self, in_c, out_c, k, s=1, p=0):
        super(ConvBlock, self).__init__()
        self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p)
        self.bn = nn.BatchNorm2d(out_c)

    def forward(self, x):
        return F.relu(self.bn(self.conv(x)))


class InceptionA(nn.Module):

    def __init__(self, in_channels, out_channels):
        super(InceptionA, self).__init__()
        mid_channels = out_channels // 4

        self.stream1 = nn.Sequential(
            ConvBlock(in_channels, mid_channels, 1),
            ConvBlock(mid_channels, mid_channels, 3, p=1),
        )
        self.stream2 = nn.Sequential(
            ConvBlock(in_channels, mid_channels, 1),
            ConvBlock(mid_channels, mid_channels, 3, p=1),
        )
        self.stream3 = nn.Sequential(
            ConvBlock(in_channels, mid_channels, 1),
            ConvBlock(mid_channels, mid_channels, 3, p=1),
        )
        self.stream4 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1),
            ConvBlock(in_channels, mid_channels, 1),
        )

    def forward(self, x):
        s1 = self.stream1(x)
        s2 = self.stream2(x)
        s3 = self.stream3(x)
        s4 = self.stream4(x)
        y = torch.cat([s1, s2, s3, s4], dim=1)
        return y


class InceptionB(nn.Module):

    def __init__(self, in_channels, out_channels):
        super(InceptionB, self).__init__()
        mid_channels = out_channels // 4

        self.stream1 = nn.Sequential(
            ConvBlock(in_channels, mid_channels, 1),
            ConvBlock(mid_channels, mid_channels, 3, s=2, p=1),
        )
        self.stream2 = nn.Sequential(
            ConvBlock(in_channels, mid_channels, 1),
            ConvBlock(mid_channels, mid_channels, 3, p=1),
            ConvBlock(mid_channels, mid_channels, 3, s=2, p=1),
        )
        self.stream3 = nn.Sequential(
            nn.MaxPool2d(3, stride=2, padding=1),
            ConvBlock(in_channels, mid_channels * 2, 1),
        )

    def forward(self, x):
        s1 = self.stream1(x)
        s2 = self.stream2(x)
        s3 = self.stream3(x)
        y = torch.cat([s1, s2, s3], dim=1)
        return y


class SpatialAttn(nn.Module):
    """Spatial Attention (Sec. 3.1.I.1)"""

    def __init__(self):
        super(SpatialAttn, self).__init__()
        self.conv1 = ConvBlock(1, 1, 3, s=2, p=1)
        self.conv2 = ConvBlock(1, 1, 1)

    def forward(self, x):
        # global cross-channel averaging
        x = x.mean(1, keepdim=True)
        # 3-by-3 conv
        x = self.conv1(x)
        # bilinear resizing
        x = F.upsample(
            x, (x.size(2) * 2, x.size(3) * 2),
            mode='bilinear',
            align_corners=True
        )
        # scaling conv
        x = self.conv2(x)
        return x


class ChannelAttn(nn.Module):
    """Channel Attention (Sec. 3.1.I.2)"""

    def __init__(self, in_channels, reduction_rate=16):
        super(ChannelAttn, self).__init__()
        assert in_channels % reduction_rate == 0
        self.conv1 = ConvBlock(in_channels, in_channels // reduction_rate, 1)
        self.conv2 = ConvBlock(in_channels // reduction_rate, in_channels, 1)

    def forward(self, x):
        # squeeze operation (global average pooling)
        x = F.avg_pool2d(x, x.size()[2:])
        # excitation operation (2 conv layers)
        x = self.conv1(x)
        x = self.conv2(x)
        return x


class SoftAttn(nn.Module):
    """Soft Attention (Sec. 3.1.I)
    
    Aim: Spatial Attention + Channel Attention
    
    Output: attention maps with shape identical to input.
    """

    def __init__(self, in_channels):
        super(SoftAttn, self).__init__()
        self.spatial_attn = SpatialAttn()
        self.channel_attn = ChannelAttn(in_channels)
        self.conv = ConvBlock(in_channels, in_channels, 1)

    def forward(self, x):
        y_spatial = self.spatial_attn(x)
        y_channel = self.channel_attn(x)
        y = y_spatial * y_channel
        y = torch.sigmoid(self.conv(y))
        return y


class HardAttn(nn.Module):
    """Hard Attention (Sec. 3.1.II)"""

    def __init__(self, in_channels):
        super(HardAttn, self).__init__()
        self.fc = nn.Linear(in_channels, 4 * 2)
        self.init_params()

    def init_params(self):
        self.fc.weight.data.zero_()
        self.fc.bias.data.copy_(
            torch.tensor(
                [0, -0.75, 0, -0.25, 0, 0.25, 0, 0.75], dtype=torch.float
            )
        )

    def forward(self, x):
        # squeeze operation (global average pooling)
        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), x.size(1))
        # predict transformation parameters
        theta = torch.tanh(self.fc(x))
        theta = theta.view(-1, 4, 2)
        return theta


class HarmAttn(nn.Module):
    """Harmonious Attention (Sec. 3.1)"""

    def __init__(self, in_channels):
        super(HarmAttn, self).__init__()
        self.soft_attn = SoftAttn(in_channels)
        self.hard_attn = HardAttn(in_channels)

    def forward(self, x):
        y_soft_attn = self.soft_attn(x)
        theta = self.hard_attn(x)
        return y_soft_attn, theta


class HACNN(nn.Module):
    """Harmonious Attention Convolutional Neural Network.

    Reference:
        Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.

    Public keys:
        - ``hacnn``: HACNN.
    """

    # Args:
    #    num_classes (int): number of classes to predict
    #    nchannels (list): number of channels AFTER concatenation
    #    feat_dim (int): feature dimension for a single stream
    #    learn_region (bool): whether to learn region features (i.e. local branch)

    def __init__(
        self,
        num_classes,
        loss='softmax',
        nchannels=[128, 256, 384],
        feat_dim=512,
        learn_region=True,
        use_gpu=True,
        **kwargs
    ):
        super(HACNN, self).__init__()
        self.loss = loss
        self.learn_region = learn_region
        self.use_gpu = use_gpu

        self.conv = ConvBlock(3, 32, 3, s=2, p=1)

        # Construct Inception + HarmAttn blocks
        # ============== Block 1 ==============
        self.inception1 = nn.Sequential(
            InceptionA(32, nchannels[0]),
            InceptionB(nchannels[0], nchannels[0]),
        )
        self.ha1 = HarmAttn(nchannels[0])

        # ============== Block 2 ==============
        self.inception2 = nn.Sequential(
            InceptionA(nchannels[0], nchannels[1]),
            InceptionB(nchannels[1], nchannels[1]),
        )
        self.ha2 = HarmAttn(nchannels[1])

        # ============== Block 3 ==============
        self.inception3 = nn.Sequential(
            InceptionA(nchannels[1], nchannels[2]),
            InceptionB(nchannels[2], nchannels[2]),
        )
        self.ha3 = HarmAttn(nchannels[2])

        self.fc_global = nn.Sequential(
            nn.Linear(nchannels[2], feat_dim),
            nn.BatchNorm1d(feat_dim),
            nn.ReLU(),
        )
        self.classifier_global = nn.Linear(feat_dim, num_classes)

        if self.learn_region:
            self.init_scale_factors()
            self.local_conv1 = InceptionB(32, nchannels[0])
            self.local_conv2 = InceptionB(nchannels[0], nchannels[1])
            self.local_conv3 = InceptionB(nchannels[1], nchannels[2])
            self.fc_local = nn.Sequential(
                nn.Linear(nchannels[2] * 4, feat_dim),
                nn.BatchNorm1d(feat_dim),
                nn.ReLU(),
            )
            self.classifier_local = nn.Linear(feat_dim, num_classes)
            self.feat_dim = feat_dim * 2
        else:
            self.feat_dim = feat_dim

    def init_scale_factors(self):
        # initialize scale factors (s_w, s_h) for four regions
        self.scale_factors = []
        self.scale_factors.append(
            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
        )
        self.scale_factors.append(
            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
        )
        self.scale_factors.append(
            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
        )
        self.scale_factors.append(
            torch.tensor([[1, 0], [0, 0.25]], dtype=torch.float)
        )

    def stn(self, x, theta):
        """Performs spatial transform
        
        x: (batch, channel, height, width)
        theta: (batch, 2, 3)
        """
        grid = F.affine_grid(theta, x.size())
        x = F.grid_sample(x, grid)
        return x

    def transform_theta(self, theta_i, region_idx):
        """Transforms theta to include (s_w, s_h), resulting in (batch, 2, 3)"""
        scale_factors = self.scale_factors[region_idx]
        theta = torch.zeros(theta_i.size(0), 2, 3)
        theta[:, :, :2] = scale_factors
        theta[:, :, -1] = theta_i
        if self.use_gpu:
            theta = theta.cuda()
        return theta

    def forward(self, x):
        assert x.size(2) == 160 and x.size(3) == 64, \
            'Input size does not match, expected (160, 64) but got ({}, {})'.format(x.size(2), x.size(3))
        x = self.conv(x)

        # ============== Block 1 ==============
        # global branch
        x1 = self.inception1(x)
        x1_attn, x1_theta = self.ha1(x1)
        x1_out = x1 * x1_attn
        # local branch
        if self.learn_region:
            x1_local_list = []
            for region_idx in range(4):
                x1_theta_i = x1_theta[:, region_idx, :]
                x1_theta_i = self.transform_theta(x1_theta_i, region_idx)
                x1_trans_i = self.stn(x, x1_theta_i)
                x1_trans_i = F.upsample(
                    x1_trans_i, (24, 28), mode='bilinear', align_corners=True
                )
                x1_local_i = self.local_conv1(x1_trans_i)
                x1_local_list.append(x1_local_i)

        # ============== Block 2 ==============
        # Block 2
        # global branch
        x2 = self.inception2(x1_out)
        x2_attn, x2_theta = self.ha2(x2)
        x2_out = x2 * x2_attn
        # local branch
        if self.learn_region:
            x2_local_list = []
            for region_idx in range(4):
                x2_theta_i = x2_theta[:, region_idx, :]
                x2_theta_i = self.transform_theta(x2_theta_i, region_idx)
                x2_trans_i = self.stn(x1_out, x2_theta_i)
                x2_trans_i = F.upsample(
                    x2_trans_i, (12, 14), mode='bilinear', align_corners=True
                )
                x2_local_i = x2_trans_i + x1_local_list[region_idx]
                x2_local_i = self.local_conv2(x2_local_i)
                x2_local_list.append(x2_local_i)

        # ============== Block 3 ==============
        # Block 3
        # global branch
        x3 = self.inception3(x2_out)
        x3_attn, x3_theta = self.ha3(x3)
        x3_out = x3 * x3_attn
        # local branch
        if self.learn_region:
            x3_local_list = []
            for region_idx in range(4):
                x3_theta_i = x3_theta[:, region_idx, :]
                x3_theta_i = self.transform_theta(x3_theta_i, region_idx)
                x3_trans_i = self.stn(x2_out, x3_theta_i)
                x3_trans_i = F.upsample(
                    x3_trans_i, (6, 7), mode='bilinear', align_corners=True
                )
                x3_local_i = x3_trans_i + x2_local_list[region_idx]
                x3_local_i = self.local_conv3(x3_local_i)
                x3_local_list.append(x3_local_i)

        # ============== Feature generation ==============
        # global branch
        x_global = F.avg_pool2d(x3_out,
                                x3_out.size()[2:]
                                ).view(x3_out.size(0), x3_out.size(1))
        x_global = self.fc_global(x_global)
        # local branch
        if self.learn_region:
            x_local_list = []
            for region_idx in range(4):
                x_local_i = x3_local_list[region_idx]
                x_local_i = F.avg_pool2d(x_local_i,
                                         x_local_i.size()[2:]
                                         ).view(x_local_i.size(0), -1)
                x_local_list.append(x_local_i)
            x_local = torch.cat(x_local_list, 1)
            x_local = self.fc_local(x_local)

        if not self.training:
            # l2 normalization before concatenation
            if self.learn_region:
                x_global = x_global / x_global.norm(p=2, dim=1, keepdim=True)
                x_local = x_local / x_local.norm(p=2, dim=1, keepdim=True)
                return torch.cat([x_global, x_local], 1)
            else:
                return x_global

        prelogits_global = self.classifier_global(x_global)
        if self.learn_region:
            prelogits_local = self.classifier_local(x_local)

        if self.loss == 'softmax':
            if self.learn_region:
                return (prelogits_global, prelogits_local)
            else:
                return prelogits_global

        elif self.loss == 'triplet':
            if self.learn_region:
                return (prelogits_global, prelogits_local), (x_global, x_local)
            else:
                return prelogits_global, x_global

        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))
